ABSTRACT: Neuroelectric oscillations reflect synchronous excitability fluctuations in ensembles of neurons, ubiquitous in the waking (and sleeping) brain, and are believed to be fundamental instruments in adaptive brain function. Through oscillatory phase reset and entrainment, the large delta (1-3 Hz) and theta (4-8 Hz) oscillations that dominate the spontaneous activity spectrum in auditory cortex can be harnessed as tools that allow the brain to parse, select and represent rhythmic event streams ranging from simple pure tones to multiscale patterns like speech. Despite the demonstrable importance of oscillatory entrainment in selective attention and other cognitive brain operations, physiological mechanisms underlying these dynamical processes remain poorly understood. Of fundamental importance, a wide range of findings converge on the idea that established neuronal ensembles operate according to transient activation cycles. While flexible, activation cycles are physiologically constrained to operate within certain dynamic resonance ranges. These cycles both frame transient sensory evoked responses and form the building blocks of brain rhythms. We propose to develop a combination of computational and physiological methods. Our long-term goal is a mechanistic, physiological understanding of the ways that neuronal dynamics generate cognitive abilities. As an initial exploratory step, we will develop an iterative exchange between computational and physiological investigations focusing on primary auditory cortex (A1). We will address the dynamic cellular mechanisms underlying the tendency of A1 ensembles to oscillate near preferred resonance frequencies, and those causing ensembles? entrainment to behaviorally relevant event streams. The more specific goals are: 1) Devise a mechanistic computational model of A1 incorporating key dynamics, 2) Test the model?s starting specifications and iteratively refine them by recordings in primary auditory cortex of monkeys during performance of auditory discriminations, and 3) Evaluate the model?s predictions by manipulation of A1 physiology using nerve stimulation methods that can impact both A1 dynamics and behavior. The proposed merger of computational and ensemble physiology methods is a novel venture, with admittedly high-risk components. However, success in any of our aims will improve the mechanistic understanding of disorders such as schizophrenia, in which sensory entrainment at both low and high frequencies is demonstrably impaired. Findings in these exploratory studies will support and inform a broader effort to develop a more detailed concrete picture of the properties of neuronal ensembles that create brain rhythms and organize them to perform fundamental cognitive operations.